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000856136 1001_ $$0P:(DE-Juel1)168456$$aBasiri, Farnaz$$b0$$ufzj
000856136 245__ $$aInferring power-grid topology in the face of uncertainties
000856136 260__ $$aWoodbury, NY$$bInst.$$c2018
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000856136 520__ $$aWe develop methods to efficiently reconstruct the topology and line parameters of a power grid from the measurement of nodal variables. We propose two compressed sensing algorithms that minimize the amount of necessary measurement resources by exploiting network sparsity, symmetry of connections, and potential prior knowledge about the connectivity. The algorithms are reciprocal to established state estimation methods, where nodal variables are estimated from few measurements given the network structure. Hence, they enable an advanced grid monitoring where both state and structure of a grid are subject to uncertainties or missing information.
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000856136 7001_ $$0P:(DE-HGF)0$$aTimme, Marc$$b2
000856136 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b3$$eCorresponding author$$ufzj
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